
import dash
from dash import dcc
from dash import html
from dash.dependencies import Input, Output
import pandas as pd
import plotly.express as px
import numpy as np
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')


pd.options.display.max_rows=10


matches = pd.read_csv('IPL Matches 2008-2020.csv')


#Find the sum of duplicate
matches.duplicated().sum()

0


#Find the number of unique data in each column
matches.nunique()

id                 816
city                32
date               596
player_of_match    233
venue               36
                  ... 
result_margin       91
eliminator           2
method               1
umpire1             48
umpire2             47
Length: 17, dtype: int64


# checking null values in the data
matches.isna().sum()

id                   0
city                13
date                 0
player_of_match      4
venue                0
                  ... 
result_margin       17
eliminator           4
method             797
umpire1              0
umpire2              0
Length: 17, dtype: int64


## in percentage
(matches.isna().sum())/(len(matches))*100

id                  0.000000
city                1.593137
date                0.000000
player_of_match     0.490196
venue               0.000000
                     ...    
result_margin       2.083333
eliminator          0.490196
method             97.671569
umpire1             0.000000
umpire2             0.000000
Length: 17, dtype: float64


##see descriptive analysis
matches.describe()


#checking nulls on region column
matches[matches.city.isnull().values]


#Extracting the table with match result='tie'
matches[matches.result=='tie']


matches.head()


#Get the data for players got most "man of match" awards
matches['player_of_match'].value_counts()

AB de Villiers    23
CH Gayle          22
RG Sharma         18
DA Warner         17
MS Dhoni          17
                  ..
S Anirudha         1
M Kartik           1
R McLaren          1
RJ Harris          1
PJ Cummins         1
Name: player_of_match, Length: 233, dtype: int64


matches['player_of_match'].value_counts()[0:10]

AB de Villiers    23
CH Gayle          22
RG Sharma         18
DA Warner         17
MS Dhoni          17
SR Watson         16
YK Pathan         16
SK Raina          14
V Kohli           13
G Gambhir         13
Name: player_of_match, dtype: int64


#list with number of times players got "man of match" award
list(matches['player_of_match'].value_counts()[0:5])

[23, 22, 18, 17, 17]


#list of top 5 players got "man of match" award
list(matches['player_of_match'].value_counts()[0:5].keys())

['AB de Villiers', 'CH Gayle', 'RG Sharma', 'DA Warner', 'MS Dhoni']


import matplotlib.pyplot as plt


plt.title('Player of match',fontname='Times New Roman')
plt.bar(list(matches['player_of_match'].value_counts()[0:5].keys()),list(matches['player_of_match'].value_counts()[0:5]),color="r")
plt.figure(figsize=(8,5))
plt.show()

<Figure size 800x500 with 0 Axes>


matches['Winner'] = matches['result_margin'].astype(str)+'-'+matches['result']


matches


#Extracting the matches wins by "runs"
bat_first=matches[matches.result=='runs']


bat_first


top_bat_first=bat_first.sort_values("result_margin",ascending=False)
top_bat_first.head()


plt.hist(bat_first['result_margin'])
plt.show()


matches['year']=pd.DatetimeIndex(matches['date']).year
matches.head()


#find the total match count and group it by year
match_season=matches.groupby(['year'])['id'].count().reset_index().rename(columns={'id':'match_count'})
match_season.style.background_gradient(cmap='BuGn')


import plotly
import plotly.express as px


colors = ['mediumblue',] * 13
colors[5] = 'crimson'
fig=px.bar(data_frame=match_season,x=match_season.year,y=match_season.match_count,labels=dict(x="year",y="match_count"),)
fig.update_layout(title="Number of matches played in different years ",
                  titlefont={'size': 26},template='simple_white'     
                  )
fig.update_traces(marker_line_color='black',
                  marker_line_width=2.5, opacity=1,marker_color=colors)
fig.show()


max_matches_won=matches['winner'].value_counts()


plt.figure(figsize=(9,6))
sns.barplot(x=max_matches_won,y=max_matches_won.keys())
plt.xlabel('Number of matches')
plt.title('Number of times a team won the match',pad=15)

Text(0.5, 1.0, 'Number of times a team won the match')


sns.pairplot(matches,hue='winner')

<seaborn.axisgrid.PairGrid at 0x1f8dcc40880>


win = ['Chennai Super Kings']
win

['Chennai Super Kings']


match_won=matches['winner'].value_counts()


match_won

Mumbai Indians                 120
Chennai Super Kings            106
Kolkata Knight Riders           99
Royal Challengers Bangalore     91
Kings XI Punjab                 88
                              ... 
Gujarat Lions                   13
Pune Warriors                   12
Rising Pune Supergiant          10
Kochi Tuskers Kerala             6
Rising Pune Supergiants          5
Name: winner, Length: 15, dtype: int64


matches['year']=pd.DatetimeIndex(matches['date']).year


matches['year'].head()

0    2008
1    2008
2    2008
3    2008
4    2008
Name: year, dtype: int64


# Extract the winner data and group it by year
matches_times = matches.groupby(['year','winner'])['id'].count().reset_index().rename(columns={'id':'win_times'})
matches_times


times = matches_times['winner'] == win[0]
px.line(matches_times[times], x='year', y='win_times',template='plotly_dark',color_discrete_sequence=['blue'])


## Extract the number of matches win by runs
bat_first=matches[matches.result=='runs']
bat_first.head()


matches['year']=pd.DatetimeIndex(matches['date']).year


matches['year'].head()

0    2008
1    2008
2    2008
3    2008
4    2008
Name: year, dtype: int64


bat_first['winner'].value_counts()

Mumbai Indians                 62
Chennai Super Kings            53
Kolkata Knight Riders          41
Kings XI Punjab                40
Royal Challengers Bangalore    38
                               ..
Pune Warriors                   6
Rising Pune Supergiant          5
Kochi Tuskers Kerala            2
Rising Pune Supergiants         2
Gujarat Lions                   1
Name: winner, Length: 15, dtype: int64


# Extract the number of matches win by runs and group it by year
run_times = bat_first.groupby(['year','winner'])['id'].count().reset_index().rename(columns={'id':'run_times'})
run_times


run_times_1 = run_times.rename(columns={'winner':'team1'})


run_times_1


times_bat = run_times['winner'] == win[0]
px.bar(run_times[times_bat], x='year', y='run_times',template='plotly_dark',color_discrete_sequence=['yellow'])


## Extract the number of matches win by wickets
bowl_first = matches[matches.result=='wickets']
bowl_first.head()


matches['year']=pd.DatetimeIndex(matches['date']).year


matches['year'].head()

0    2008
1    2008
2    2008
3    2008
4    2008
Name: year, dtype: int64


bowl_first['winner'].value_counts()

Kolkata Knight Riders          57
Mumbai Indians                 56
Chennai Super Kings            53
Royal Challengers Bangalore    51
Rajasthan Royals               51
                               ..
Delhi Capitals                  8
Pune Warriors                   6
Rising Pune Supergiant          5
Kochi Tuskers Kerala            4
Rising Pune Supergiants         3
Name: winner, Length: 15, dtype: int64


## Extract the number of matches win by wickets and group it by year
bowl_times = bowl_first.groupby(['year','winner'])['id'].count().reset_index().rename(columns={'id':'win_bybowl'})
bowl_times


bowl_times_1 = bowl_times.rename(columns={'winner':'team1'})
bowl_times_1


#import numpy as np
#win_bowl = px.data.gapminder().query("year == 2018")
win_bowl= bowl_times['winner'] == win[0]
fig = px.treemap(bowl_times[win_bowl], path=[px.Constant(win[0]), 'year', 'win_bybowl'],values='year',color='win_bybowl',hover_data=['year'],color_discrete_sequence=['turquoise'],color_continuous_scale='PuBu')
fig.update_traces(root_color="turquoise")
fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig.show()


matches.head()


matches['year']=pd.DatetimeIndex(matches['date']).year


matches['year'].head()

0    2008
1    2008
2    2008
3    2008
4    2008
Name: year, dtype: int64


matches['winner'].value_counts()

Mumbai Indians                 120
Chennai Super Kings            106
Kolkata Knight Riders           99
Royal Challengers Bangalore     91
Kings XI Punjab                 88
                              ... 
Gujarat Lions                   13
Pune Warriors                   12
Rising Pune Supergiant          10
Kochi Tuskers Kerala             6
Rising Pune Supergiants          5
Name: winner, Length: 15, dtype: int64


## Extract the number of matches win and group it by year
win_times = matches.groupby(['year','winner'])['id'].count().reset_index().rename(columns={'id':'winner_times'})
win_times


matches['year'].value_counts()

2013    76
2012    74
2011    73
2010    60
2014    60
        ..
2020    60
2015    59
2017    59
2008    58
2009    57
Name: year, Length: 13, dtype: int64


# Extract the number of matches lost and group it by year
win_times_percent_1 = matches.groupby(['year','team2'])['id'].count().reset_index().rename(columns={'id':'winner_times_percent'})
win_times_percent_1


win_times_percent = matches.groupby(['year','team1'])['id'].count().reset_index().rename(columns={'id':'winner_times_percent_1'})
win_times_percent


win_times_percent_1 = win_times_percent_1.rename(columns={'team2':'team1'})


win_times_percent_1


# merging win and lost details in a single table
wins=pd.merge(win_times_percent,win_times_percent_1)


wins


#finding the number of total matches by adding number of times win and number of times lost
wins['total'] = wins['winner_times_percent_1'] + wins['winner_times_percent']


wins


win_times


win_2 = win_times.rename(columns={'winner':'team1'})
win_2


#merging the value of number of times win and total with the previous table containing details of number of times wins, lost.
win_1=pd.merge(wins,win_2)


win_1


# finding the number of times "win by wickets" by subtracting the values of "win by run" from "total"
win_1['loss'] = win_1['total'] - win_1['winner_times']


win_1


win_1.drop(['winner_times_percent_1','winner_times_percent'],axis=1)


win_3=win_1.drop(['total'],axis=1)
win_3


#merging win by runs and win by bowl in a single table
win_5=pd.merge(run_times_1,bowl_times_1)
win_5


#merging the column for dashboard
win_6=pd.merge(win_3,win_5)
win_6


import os
os.getcwd()

'C:\\Users\\Maiyuri'


win_7=win_6.drop(['winner_times_percent_1','winner_times_percent'],axis=1)
win_7


wins=['Chennai Super Kings']
win_8=win_7['team1'] == wins[0]
fig_1=px.pie(win_7[win_8],'year','winner_times',hole=.3)
fig_2=px.pie(win_7[win_8],'year','loss')
#fig_3=px.bar(run_times[times_bat], x='year', y='run_times',template='plotly_dark',color_discrete_sequence=['yellow'])
fig_3=px.bar(win_7[win_8],x='year',y='winner_times',template='plotly_dark',color_discrete_sequence=['yellow'])
fig_4=px.line(win_7[win_8],x='year',y='run_times',template='plotly_dark',color_discrete_sequence=['blue'])
fig_5 = px.treemap(win_7[win_8], path=[px.Constant(wins[0]), 'year', 'win_bybowl'],values='year',color='win_bybowl',hover_data=['year'],color_discrete_sequence=['turquoise'],color_continuous_scale='PuBu')
fig_5.update_traces(root_color="turquoise")
fig_5.update_layout(margin = dict(t=50, l=25, r=25, b=25))
fig_4.show()
fig_5.show()
fig_1.show()
fig_2.show()
fig_3.show()

	id	neutral_venue	result_margin
count	8.160000e+02	816.000000	799.000000
mean	7.563496e+05	0.094363	17.321652
std	3.058943e+05	0.292512	22.068427
min	3.359820e+05	0.000000	1.000000
25%	5.012278e+05	0.000000	6.000000
50%	7.292980e+05	0.000000	8.000000
75%	1.082626e+06	0.000000	19.500000
max	1.237181e+06	1.000000	146.000000

	id	city	date	player_of_match	venue	neutral_venue	team1	team2	toss_winner	toss_decision	winner	result	result_margin	eliminator	method	umpire1	umpire2
399	729281	NaN	2014-04-17	YS Chahal	Sharjah Cricket Stadium	1	Delhi Daredevils	Royal Challengers Bangalore	Royal Challengers Bangalore	field	Royal Challengers Bangalore	wickets	8.0	N	NaN	Aleem Dar	S Ravi
402	729287	NaN	2014-04-19	PA Patel	Dubai International Cricket Stadium	1	Royal Challengers Bangalore	Mumbai Indians	Royal Challengers Bangalore	field	Royal Challengers Bangalore	wickets	7.0	N	NaN	Aleem Dar	AK Chaudhary
403	729289	NaN	2014-04-19	JP Duminy	Dubai International Cricket Stadium	1	Kolkata Knight Riders	Delhi Daredevils	Kolkata Knight Riders	bat	Delhi Daredevils	wickets	4.0	N	NaN	Aleem Dar	VA Kulkarni
404	729291	NaN	2014-04-20	GJ Maxwell	Sharjah Cricket Stadium	1	Rajasthan Royals	Kings XI Punjab	Kings XI Punjab	field	Kings XI Punjab	wickets	7.0	N	NaN	BF Bowden	M Erasmus
406	729295	NaN	2014-04-22	GJ Maxwell	Sharjah Cricket Stadium	1	Kings XI Punjab	Sunrisers Hyderabad	Sunrisers Hyderabad	field	Kings XI Punjab	runs	72.0	N	NaN	M Erasmus	S Ravi
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
410	729303	NaN	2014-04-25	MM Sharma	Dubai International Cricket Stadium	1	Chennai Super Kings	Mumbai Indians	Mumbai Indians	bat	Chennai Super Kings	wickets	7.0	N	NaN	BF Bowden	M Erasmus
413	729309	NaN	2014-04-27	M Vijay	Sharjah Cricket Stadium	1	Delhi Daredevils	Mumbai Indians	Mumbai Indians	bat	Delhi Daredevils	wickets	6.0	N	NaN	Aleem Dar	VA Kulkarni
414	729311	NaN	2014-04-27	DR Smith	Sharjah Cricket Stadium	1	Sunrisers Hyderabad	Chennai Super Kings	Sunrisers Hyderabad	bat	Chennai Super Kings	wickets	5.0	N	NaN	AK Chaudhary	VA Kulkarni
415	729313	NaN	2014-04-28	Sandeep Sharma	Dubai International Cricket Stadium	1	Kings XI Punjab	Royal Challengers Bangalore	Kings XI Punjab	field	Kings XI Punjab	wickets	5.0	N	NaN	BF Bowden	S Ravi
417	729317	NaN	2014-04-30	B Kumar	Dubai International Cricket Stadium	1	Mumbai Indians	Sunrisers Hyderabad	Mumbai Indians	field	Sunrisers Hyderabad	runs	15.0	N	NaN	HDPK Dharmasena	M Erasmus

	id	city	date	player_of_match	venue	neutral_venue	team1	team2	toss_winner	toss_decision	winner	result	result_margin	eliminator	method	umpire1	umpire2
66	392190	Cape Town	2009-04-23	YK Pathan	Newlands	1	Kolkata Knight Riders	Rajasthan Royals	Kolkata Knight Riders	field	Rajasthan Royals	tie	NaN	Y	NaN	MR Benson	M Erasmus
130	419121	Chennai	2010-03-21	J Theron	MA Chidambaram Stadium, Chepauk	0	Chennai Super Kings	Kings XI Punjab	Chennai Super Kings	field	Kings XI Punjab	tie	NaN	Y	NaN	K Hariharan	DJ Harper
328	598004	Hyderabad	2013-04-07	GH Vihari	Rajiv Gandhi International Stadium, Uppal	0	Sunrisers Hyderabad	Royal Challengers Bangalore	Royal Challengers Bangalore	bat	Sunrisers Hyderabad	tie	NaN	Y	NaN	AK Chaudhary	S Ravi
341	598017	Bangalore	2013-04-16	V Kohli	M Chinnaswamy Stadium	0	Royal Challengers Bangalore	Delhi Daredevils	Royal Challengers Bangalore	field	Royal Challengers Bangalore	tie	NaN	Y	NaN	M Erasmus	VA Kulkarni
416	729315	Abu Dhabi	2014-04-29	JP Faulkner	Sheikh Zayed Stadium	1	Kolkata Knight Riders	Rajasthan Royals	Rajasthan Royals	bat	Rajasthan Royals	tie	NaN	Y	NaN	Aleem Dar	AK Chaudhary
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
746	1178426	Mumbai	2019-05-02	JJ Bumrah	Wankhede Stadium	0	Mumbai Indians	Sunrisers Hyderabad	Mumbai Indians	bat	Mumbai Indians	tie	NaN	Y	NaN	CK Nandan	S Ravi
757	1216493	Dubai	2020-09-20	MP Stoinis	Dubai International Cricket Stadium	0	Delhi Capitals	Kings XI Punjab	Kings XI Punjab	field	Delhi Capitals	tie	NaN	Y	NaN	AK Chaudhary	Nitin Menon
776	1216512	Abu Dhabi	2020-10-18	LH Ferguson	Sheikh Zayed Stadium	0	Kolkata Knight Riders	Sunrisers Hyderabad	Sunrisers Hyderabad	field	Kolkata Knight Riders	tie	NaN	Y	NaN	PG Pathak	S Ravi
781	1216517	Dubai	2020-10-18	KL Rahul	Dubai International Cricket Stadium	0	Mumbai Indians	Kings XI Punjab	Mumbai Indians	bat	Kings XI Punjab	tie	NaN	Y	NaN	Nitin Menon	PR Reiffel
811	1216547	Dubai	2020-09-28	AB de Villiers	Dubai International Cricket Stadium	0	Royal Challengers Bangalore	Mumbai Indians	Mumbai Indians	field	Royal Challengers Bangalore	tie	NaN	Y	NaN	Nitin Menon	PR Reiffel

	id	city	date	player_of_match	venue	team1	team2	toss_winner	toss_decision	winner	result	result_margin	eliminator	method	umpire1	umpire2
0	335982	Bangalore	2008-04-18	BB McCullum	M Chinnaswamy Stadium	Royal Challengers Bangalore	Kolkata Knight Riders	Royal Challengers Bangalore	field	Kolkata Knight Riders	runs	140.0	N	NaN	Asad Rauf	RE Koertzen
1	335983	Chandigarh	2008-04-19	MEK Hussey	Punjab Cricket Association Stadium, Mohali	Kings XI Punjab	Chennai Super Kings	Chennai Super Kings	bat	Chennai Super Kings	runs	33.0	N	NaN	MR Benson	SL Shastri
2	335984	Delhi	2008-04-19	MF Maharoof	Feroz Shah Kotla	Delhi Daredevils	Rajasthan Royals	Rajasthan Royals	bat	Delhi Daredevils	wickets	9.0	N	NaN	Aleem Dar	GA Pratapkumar
3	335985	Mumbai	2008-04-20	MV Boucher	Wankhede Stadium	Mumbai Indians	Royal Challengers Bangalore	Mumbai Indians	bat	Royal Challengers Bangalore	wickets	5.0	N	NaN	SJ Davis	DJ Harper
4	335986	Kolkata	2008-04-20	DJ Hussey	Eden Gardens	Kolkata Knight Riders	Deccan Chargers	Deccan Chargers	bat	Kolkata Knight Riders	wickets	5.0	N	NaN	BF Bowden	K Hariharan

	id	city	date	player_of_match	venue	neutral_venue	team1	team2	toss_winner	toss_decision	winner	result	result_margin	eliminator	method	umpire1	umpire2	Winner
0	335982	Bangalore	2008-04-18	BB McCullum	M Chinnaswamy Stadium	0	Royal Challengers Bangalore	Kolkata Knight Riders	Royal Challengers Bangalore	field	Kolkata Knight Riders	runs	140.0	N	NaN	Asad Rauf	RE Koertzen	140.0-runs
1	335983	Chandigarh	2008-04-19	MEK Hussey	Punjab Cricket Association Stadium, Mohali	0	Kings XI Punjab	Chennai Super Kings	Chennai Super Kings	bat	Chennai Super Kings	runs	33.0	N	NaN	MR Benson	SL Shastri	33.0-runs
2	335984	Delhi	2008-04-19	MF Maharoof	Feroz Shah Kotla	0	Delhi Daredevils	Rajasthan Royals	Rajasthan Royals	bat	Delhi Daredevils	wickets	9.0	N	NaN	Aleem Dar	GA Pratapkumar	9.0-wickets
3	335985	Mumbai	2008-04-20	MV Boucher	Wankhede Stadium	0	Mumbai Indians	Royal Challengers Bangalore	Mumbai Indians	bat	Royal Challengers Bangalore	wickets	5.0	N	NaN	SJ Davis	DJ Harper	5.0-wickets
4	335986	Kolkata	2008-04-20	DJ Hussey	Eden Gardens	0	Kolkata Knight Riders	Deccan Chargers	Deccan Chargers	bat	Kolkata Knight Riders	wickets	5.0	N	NaN	BF Bowden	K Hariharan	5.0-wickets
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
811	1216547	Dubai	2020-09-28	AB de Villiers	Dubai International Cricket Stadium	0	Royal Challengers Bangalore	Mumbai Indians	Mumbai Indians	field	Royal Challengers Bangalore	tie	NaN	Y	NaN	Nitin Menon	PR Reiffel	nan-tie
812	1237177	Dubai	2020-11-05	JJ Bumrah	Dubai International Cricket Stadium	0	Mumbai Indians	Delhi Capitals	Delhi Capitals	field	Mumbai Indians	runs	57.0	N	NaN	CB Gaffaney	Nitin Menon	57.0-runs
813	1237178	Abu Dhabi	2020-11-06	KS Williamson	Sheikh Zayed Stadium	0	Royal Challengers Bangalore	Sunrisers Hyderabad	Sunrisers Hyderabad	field	Sunrisers Hyderabad	wickets	6.0	N	NaN	PR Reiffel	S Ravi	6.0-wickets
814	1237180	Abu Dhabi	2020-11-08	MP Stoinis	Sheikh Zayed Stadium	0	Delhi Capitals	Sunrisers Hyderabad	Delhi Capitals	bat	Delhi Capitals	runs	17.0	N	NaN	PR Reiffel	S Ravi	17.0-runs
815	1237181	Dubai	2020-11-10	TA Boult	Dubai International Cricket Stadium	0	Delhi Capitals	Mumbai Indians	Delhi Capitals	bat	Mumbai Indians	wickets	5.0	N	NaN	CB Gaffaney	Nitin Menon	5.0-wickets

Display only the matches get tie¶

Getting the frequency of most "man of the match" awards¶

Plot a bar chart for values Player names Vs Number of matches get Player of match award.¶

making a histogram¶

Graph in plotly express for values "year" vs "Number of matches played"¶

Graph in seaborn for values "Number of matches won" vs "Team name"¶

Pairplot in seaborn¶

Line Map in plotly for "years" vs "number of win times"¶

Bar graph for "years" vs "number of times won by run"¶

Tree map for "years" and "number of times win by bowl"¶

Extracting the data to make dashboard¶

Creating the simple dashboard¶

	id	city	date	player_of_match	venue	team1	team2	toss_winner	toss_decision	winner	result	result_margin	eliminator	method	umpire1	umpire2	Winner
620	1082635	Delhi	2017-05-06	LMP Simmons	Feroz Shah Kotla	Delhi Daredevils	Mumbai Indians	Delhi Daredevils	field	Mumbai Indians	runs	146.0	N	NaN	Nitin Menon	CK Nandan	146.0-runs
560	980987	Bangalore	2016-05-14	AB de Villiers	M Chinnaswamy Stadium	Royal Challengers Bangalore	Gujarat Lions	Gujarat Lions	field	Royal Challengers Bangalore	runs	144.0	N	NaN	AY Dandekar	VK Sharma	144.0-runs
0	335982	Bangalore	2008-04-18	BB McCullum	M Chinnaswamy Stadium	Royal Challengers Bangalore	Kolkata Knight Riders	Royal Challengers Bangalore	field	Kolkata Knight Riders	runs	140.0	N	NaN	Asad Rauf	RE Koertzen	140.0-runs
497	829785	Bangalore	2015-05-06	CH Gayle	M Chinnaswamy Stadium	Royal Challengers Bangalore	Kings XI Punjab	Kings XI Punjab	field	Royal Challengers Bangalore	runs	138.0	N	NaN	RK Illingworth	VA Kulkarni	138.0-runs
351	598027	Bangalore	2013-04-23	CH Gayle	M Chinnaswamy Stadium	Royal Challengers Bangalore	Pune Warriors	Pune Warriors	field	Royal Challengers Bangalore	runs	130.0	N	NaN	Aleem Dar	C Shamshuddin	130.0-runs

	year	match_count
0	2008	58
1	2009	57
2	2010	60
3	2011	73
4	2012	74
5	2013	76
6	2014	60
7	2015	59
8	2016	60
9	2017	59
10	2018	60
11	2019	60
12	2020	60